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首页> 外文期刊>Affective Computing, IEEE Transactions on >Hierarchical Dirichlet Process Mixture Model for Music Emotion Recognition
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Hierarchical Dirichlet Process Mixture Model for Music Emotion Recognition

机译：音乐情感识别的分层狄利克雷过程混合模型

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摘要

This study proposes a novel multi-label music emotion recognition (MER) system. An emotion cannot be defined clearly in the real world because the classes of emotions are usually considered overlapping. Accordingly, this study proposes an MER system that is based on hierarchical Dirichlet process mixture model (HPDMM), whose components can be shared between models of each emotion. Moreover, the HDPMM is improved by adding a discriminant factor to the proposed system based on the concept of linear discriminant analysis. The proposed system represents an emotion using weighting coefficients that are related to a global set of components. Moreover, three methods are proposed to compute the weighting coefficients of testing data, and the weighting coefficients are used to determine whether or not the testing data contain certain emotional content. In the tasks of music emotion annotation and retrieval, experimental results show that the proposed MER system outperforms state-of-the-art systems in terms of F- score and mean average precision.
机译：这项研究提出了一种新颖的多标签音乐情感识别（MER）系统。在现实世界中无法明确定义情感，因为通常将情感类别视为重叠。因此，本研究提出了一种基于分层Dirichlet过程混合模型（HPDMM）的MER系统，其组件可以在每种情感的模型之间共享。此外，基于线性判别分析的概念，通过向所提出的系统添加判别因子来改进HDPMM。所提出的系统使用与全局组件集相关的加权系数来表示一种情绪。此外，提出了三种方法来计算测试数据的加权系数，并使用加权系数来确定测试数据是否包含某些情感内容。在音乐情感注释和检索任务中，实验结果表明，所提出的MER系统在F分数和平均平均精度方面优于最新系统。

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